The research focuses on the potential for organizing circular production and improving energy efficiency through decision-making based on big data and AI. The authors conducted a regression analysis on the best practices of the top 20 dynamically developing digital economies with the highest activity in applying big data and AI in 2023. The developed econometric model provided highly accurate quantitative measurements of the impact of automating investment and environmental decisions based on big data and AI technologies for the green economy, underscoring its theoretical significance. The scientific novelty of the research results is linked to the author’s classification of the consequences of automating investment and environmental decisions based on big data and AI for the green economy. Using Russia as an example, the authors demonstrated the potential for growth in energy efficiency and circularity of production by implementing big data and AI in decision-making, highlighting its practical significance by expanding opportunities for planning and forecasting the development of the green economy. The authors developed a decision-making framework for organizing energy-efficient circular production based on big data and AI. The managerial significance of the developed model is reflected in the optimization of these decisions.